Mutations of the CHEK2 Gene in Patients with Cancer and Their

F1000Research 2016, 5:2791 Last updated: 20 JUN 2019

RESEARCH ARTICLE Mutations of the CHEK2 gene in patients with cancer and their presence in the Latin American population [version 1; peer review: 3 approved with reservations] Sandra Guauque-Olarte 1, Ana-Lucia Rivera-Herrera2, Laura Cifuentes-C 1

1GIOD Group, Faculty of Dentistry, Universidad Cooperativa de Colombia, Pasto, Colombia 2Human Molecular Genetics Lab, Biology Department, Universidad del Valle, Cali, Colombia

First published: 29 Nov 2016, 5:2791 ( Open Peer Review v1 https://doi.org/10.12688/f1000research.9932.1) Latest published: 29 Nov 2016, 5:2791 ( https://doi.org/10.12688/f1000research.9932.1) Reviewer Status

Abstract Invited Reviewers Background: CHEK2 (Checkpoint Kinase 2) encodes CHK2, a 1 2 3 serine/threonine kinase involved in maintaining the G1/S and G2/M checkpoints and repair of double-strand DNA breaks via homologous version 1 recombination. Functions of CHK2 include the prevention of damaged cells published report report report from going through the cell cycle or proliferating and the maintenance of 29 Nov 2016 chromosomal stability. CHEK2 mutations have been reported in a variety of cancers including glioblastoma, ovarian, prostate, colorectal, gastric, thyroid, and lung cancer in studies performed mainly in White populations. 1 Claire Palles, NIHR Comprehensive Biomedical The most studied mutation in CHEK2 is c.1100delC, which was associated Research Centre, Oxford, UK with increased risk of breast cancer. The objective of this study was to Muhammad Usman Rashid, Department of compile mutations in CHEK2 identified in cancer genomics studies in 2 different populations and especially in Latin American individuals. Basic Sciences Research, Lahore, Pakistan Methods: A revision of cancer genomics data repositories and a profound Ewa Grzybowska, Centre of Oncology-MSC literature review of Latin American studies was performed. 3 Results: Mutations with predicted high impact in CHEK2 were reported in memorial Institute, Gliwice Branch, Wybrzeze studies from Australia, Japan, United States, among other countries. The Armii Krajowej 15, 44-101, Poland TCGA cancer types with most mutations in CHEK2 were breast, colorectal, Any reports and responses or comments on the and non-small cell lung cancer. The most common mutation found was E321* in three patients with uterine cancer. In Latin American individuals article can be found at the end of the article. nine mutations were found in melanoma, lymphoma, and head and neck cohorts from TCGA and ICGC. Latin American studies have been restricted to breast and colorectal cancer and only two mutations out of four that have been interrogated in this population were identified, namely c.1100delC and c.349A>G. Conclusions: This study presents a compilation of mutations in CHEK2 with high impact in different cancer types in White, Hispanic and other populations. We also show the necessity of screening CHEK2 mutations in Latin American in cancer types different than breast and colorectal.

Keywords CHEK2 , CHK2 , cancer , Latin America , databases , mutations , CHEK2*1100delC , genomics

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Corresponding author: Laura Cifuentes-C ([email protected]) Competing interests: No competing interests were disclosed. Grant information: LCC received funding by CONADI-Universidad Cooperativa de Colombia (Grant ID1450). Copyright: © 2016 Guauque-Olarte S et al. This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication). How to cite this article: Guauque-Olarte S, Rivera-Herrera AL and Cifuentes-C L. Mutations of the CHEK2 gene in patients with cancer and their presence in the Latin American population [version 1; peer review: 3 approved with reservations] F1000Research 2016, 5:2791 ( https://doi.org/10.12688/f1000research.9932.1) First published: 29 Nov 2016, 5:2791 (https://doi.org/10.12688/f1000research.9932.1)

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Introduction (http://fathmm.biocompute.org.uk/)34, Mutation Assessor (RRID: CHEK2 (Checkpoint Kinase 2) (OMIM +604373) encodes CHK2 SCR_005762)35 and SIFT (RID:SCR_012813)36 to compute func- a serine/threonine kinase that is the human homolog of Saccharo- tional impact scores and assign impact categories (High, Medium, myces cerevisiae RAD53 and Schizosaccharomyces pombe CDS11. Low and Unknown). The cBioPortal uses Mutation Assessor and In mammalian cells, ATM activates CHK2 in response to ionizing reports the same impact categories. We used those functional impact radiation through phosphorylation. This leads to a variety of cellular categories to filter the mutations and extract possible pathogenic responses, such as cell cycle checkpoint activation2, where CHK2 is mutations by selecting only high and medium impact mutations and involved in maintaining the G1/S and G2/M checkpoints by phos- nonsense alterations. The percentage of mutations in CHEK2 per phorylation of CDC25A, CDC25C and p533 and in the repair of cancer study and the percentage of cases altered per cancer type was double-strand DNA breaks via homologous recombination (HR) also calculated. The filter used for the ExAC information was based through phosphorylation of BRCA14 and BRCA25. CHK2 is also on the annotation of possible damaging and deleterious mutations involved in the induction of p53-dependent apoptosis through phos- made by two in silico tools: Polyphen2 (RID:SCR_013200)37 and phorylation of p53 on Ser206, and, in a p53-independent manner, SIFT36. The assessment of stop gained, splice site disrupting and via phosphorylation of PML and E2F13. These responses prevent frameshift variants was made through Loss of Function Transcript damaged cells from going through the cell cycle or proliferating. Effect Estimator (LOFTEE), a plugin of the Ensembl Variant Effect CHK2 also plays an important role during mitosis by maintaining Predictor (VEP) (RRID SCR_007931)38. The Latino annotation was chromosomal stability7. examined in the databases that reported ethnicity data; this search was done before filtering the datasets, with the purpose to report all CHEK2 c.1000delC, a truncating mutation in exon 10 that abol- genetic alterations found in Latin American populations. ishes kinase activity of the protein, was the first mutation being reported for this gene and was found in a woman with breast cancer The plots were generated with R version 3.3.1 (RRID:SCR_ and family history of Li-Fraumeni syndrome-28. The role of this 001905)39. mutation in breast cancer was confirmed by Meijers-Heijboeret al.9 and in several other studies10–22. Based on these studies, CHEK2 has Literature review of Latin American studies been proposed as a moderate penetrance breast cancer susceptibil- In order to include all the studies identifying CHEK2 gene muta- ity gene9 and mutations in this gene are associated with almost a tions in Latin America, a deep search of literature was conducted by 3-fold increase in the risk of breast cancer in women and a 10-fold using the terms “CHEK2”, “CHEK2 Latin America”, and “CHEK2 increase in the risk of breast cancer in men23. cancer” in electronic academic literature search engines. PUBMED (RRID:SCR_004846) was the relevant database used followed by Given the role of CHEK2 in maintaining genomic stability and the Google Scholar (RRID:SCR_008878). References of the retrieved fact that the CHEK2 protein is expressed in a wide range of tissues, articles were also screened for relevant studies. This search strategy it was not surprising that alterations in this protein were found in was performed iteratively up to and including 10 October 2016. other cancers, including glioblastoma, ovarian, prostate, colorectal, gastric, thyroid, and lung cancer18,24–28. The studies in CHEK2 Results included individuals mainly from the United States and Europe The complete list of mutations in CHEK2 reported in the cBioPortal while Latin American individuals were underrepresented. In order and ICGC, before applying filters, are available in Dataset 1 and to infer the role of the CHEK2 gene in the cancer etiology in the Dataset 2, respectively. Latin American population we compiled mutations in the CHEK2 gene registered in genomics data repositories and the literature, that Dataset 1. A complete list of mutations, before applying filters, in had been reported in this population. CHEK2 reported in the cBioPortal

Methods http://dx.doi.org/10.5256/f1000research.9932.d142129 Search of cancer genomics data repositories and the GWAS catalog Dataset 2. A complete list of mutations, before applying filters, in Mutations in CHEK2 were identified in The Exome Aggregation CHEK2 reported in the ICGC Consortium (ExaC, RRID:SCR_004068, http://exac.broadinsti- tute.org/)29 browser, the Cancer Genome Atlas (TCGA, RRID: http://dx.doi.org/10.5256/f1000research.9932.d142130 SCR_003193)30 data sets extracted from the cBioPortal for Can- cer Genomics (RRID:SCR_014555, www.cbioportal.org/)31, and The International Cancer Genome Consortium (ICGC) (http://icgc. CHEK2 mutations in the data genomics repositories org/)32. From the GWAS catalog (RRID:SCR_012745, https://www. cBioPortal. The available data sets consisted of 147 studies that ebi.ac.uk/gwas/)33 a list of SNPs mapped to CHEK2 and associated included only cancer samples. Mutations in CHEK2 were reported with a disease was also downloaded. Data obtained from cell line in 39 out of the 147 studies. Before applying filters, cholangiocar- studies was not included. cinoma (8.6%), uterine carcinosarcoma (7.0%), and colorectal adenocarcinoma (6.9%) were the types of cancer that showed the ICGC, the cBioportal and ExAc use prediction tools to assess func- higher number of cases (Figure 1); meanwhile, breast, colorectal tional impact of non-synonymous (SO term: missense_variant) and non-small cell lung cancer (NSCLC) had more mutations in somatic mutations on protein coding genes. ICGC uses FatHMM CHEK2 than other cancer types (Figure 2).

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Figure 1. Percentage of cases with mutations in CHEK2 per cancer study. The X axis shows the type of cancer in which at least one case has a mutation in CHECK2, the Y axis indicates the percentage of cases per study that have mutations in CHECK2 (source: cBioPortal).

Figure 2. Percentage of mutations in CHEK2 per cancer study. The X axis shows the type of cancer in which at least one mutation in CHECK2 was identified, the Y axis indicates the percentage of mutations in CHEK2 per cancer type (source: cBioPortal). n unique mutations = 159. Synonymous mutations are not included in the cBioPortal database. Page 4 of 19 F1000Research 2016, 5:2791 Last updated: 20 JUN 2019

Using the Mutation Assessor from cBioPortal, we filtered out muta- Before filtering the mutations found in the cBioPortal we tions labeled to have neutral and low impact. In Table 1 we are identified Latino individuals with the ethnicity data obtained reporting the mutations with high and medium impact and also from the TCGA clinical data available at the NCI’s Genomic Data nonsense mutations and frameshifts. Table 1 shows the 78 muta- Commons portal (GDC, RRID:SCR_014514, https://gdc-portal. tions that remained after the filtering process, 38 of which were nci.nih.gov/) (Table 2). Two patients with three mutations in the classified as with high impact. 51.2% of mutations were missense gene were found. One of the samples was a Latino patient from mutations, 20.5% were frameshift mutations, 19.2% were nonsense the head and neck squamous cell carcinoma cohort (HNSC); mutations and 9% were in splice sites. The type of cancer with this patient carries the neutral variant K373E. Because this is a most mutations (13/78) was breast cancer, followed by uterine, neutral variant it was not included in Table 2. The second lung, and colorectal cancer. The rest of cancer types had six or Latino patient was part of the diffuse large B-cell lymphoma less mutations. The most frequent mutation was E321* reported in (DLBC) cohort; this patient carries a frameshift and a nonsense three patients with uterine cancer. mutation.

Table 1. Mutations in CHEK2 identified in TCGA studies after filtering out neutral and low impact mutations.

No of AA change Type Ethnicity Type of cancer patients

A247T 1 Missense white_not hispanic or latino Uterine cancer

white_not reported Lung cancer A392V 2 Missense No data availableNo data available Melanoma

A480T 1 Missense white_not hispanic or latino Glioma and glioblastoma

A540Cfs*9 1 FS del No data availableNo data available Colorectal cancer

Lymphoid neoplasm diffuse large A98Mfs*13 1 FS ins white_hispanic or latino B-cell lymphoma [DLBC]

D208Ifs*9 1 FS del white_not hispanic or latino Lung cancer

E122* 1 Nonsense No data available Liver cancer

E275* 1 Nonsense not reported_hispanic or latino Uterine cancer

No data availableNo data available Colorectal cancer

native hawaiian or other pacific islander_ E321* 3 Nonsense not hispanic or latino Uterine cancer asian_not hispanic or latino

No data available Colorectal cancer E351D 2 Missense asian_not hispanic or latino Uterine cancer

E394Kfs*20 1 FS del white_not hispanic or latino Stomach cancer

E457Rfs*33 1 FS ins white_not hispanic or latino Breast cancer

E478* 1 Nonsense white_not hispanic or latino Thymoma

white_not reported Esophageal cancer F103L 2 Missense No data available Renal cancer

F144L 1 Missense not reported_not reported Colorectal cancer

F202Lfs*3 1 FS del not reported_not reported Stomach cancer

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No of AA change Type Ethnicity Type of cancer patients

F310V 1 Missense No data available Breast cancer

G232R 1 Missense No data available Esophageal cancer

G259Wfs*13 1 FS ins No data available Breast cancer

G306W 1 Missense white_not hispanic or latino Cervical cancer

G342V 1 Missense No data available Prostate cancer

G386V 1 Missense white_not hispanic or latino Lung cancer

H143N 1 Missense white_not hispanic or latino Renal cancer

H143Q 1 Missense asian_not reported Thyroid cancer

H282D 1 Missense white_not hispanic or latino Lung cancer

H339Y 1 Missense white_not hispanic or latino Bladder cancer

H345L 1 Missense white_not reported Esophageal cancer

H54Pfs*6 1 FS del No data available Colorectal cancer

I160M 1 Missense No data available Ewing sarcoma

I276S 1 Missense white_not reported Uterine cancer

I364T 1 Missense No data available Breast cancer

I419Yfs*4 1 FS ins No data available Colorectal cancer

K520Afs*4 1 FS del No data available Renal cancer

L226F 2 Missense No data available Head and Neck cancer

white_not hispanic or latino Sarcoma

L338R 1 Missense white_not reported Uterine cancer

L354V 1 Missense white_not reported Uterine cancer

L466Ffs*3 1 FS del not reported_not reported Lung cancer

N166S 1 Missense white_not hispanic or latino Head and Neck cancer

N185Y 1 Missense No data available Lung cancer

N290Tfs*14 1 FS del white_not hispanic or latino Liver cancer

N316Efs*2 1 FS ins No data available Colorectal cancer

P182H 1 Missense No data available Stomach cancer

P182S 1 Missense No data available Colorectal cancer

Q100* 1 Nonsense white_hispanic or latino Lymphoid neoplasm diffuse large B-cell lymphoma [DLBC]

Q29* 1 Nonsense No data available Breast cancer

Q36* 1 Nonsense No data available Colorectal cancer

R117G 1 Missense black or african american_not hispanic Lung cancer or latino

R318C 1 Missense No data available Colorectal cancer

R346C 1 Missense white_not reported Breast cancer

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No of AA change Type Ethnicity Type of cancer patients

R346G 1 Missense white_not hispanic or latino Breast cancer

No data available Breast cancer R346H 2 Missense white_not reported Ovarian cancer

R346S 1 Missense No data available Head and Neck cancer

R474H 1 Missense white_not hispanic or latino Stomach cancer

R95* 1 Nonsense white_not hispanic or latino Adrenocortical carcinoma

S210* 1 Nonsense white_not reported Uterine cancer

S223* 1 Nonsense No data available Breast cancer

S356* 1 Nonsense white_not hispanic or latino Bladder cancer

S372F 1 Missense white_not hispanic or latino Head and neck cancer

S456* 1 Nonsense No data available Pleural mesothelioma

S52P 1 Missense No data available Lung cancer

S55P 1 Missense not reported_not reported Liver cancer

T225Lfs*10 1 FS del No data available Colorectal cancer

T323Lfs*14 1 FS del white_not hispanic or latino Head and Neck cancer

T367Mfs*15 1 FS del not reported_not reported Breast cancer

V9A 1 Splice white_not hispanic or latino Uterine cancer

W97* 1 Nonsense No data available Breast cancer

W485* 1 Nonsense No data available Breast cancer

W93L 1 Missense No data available Lung cancer

X107_splice 1 Splice No data available Esophageal cancer

X198_splice 1 Splice white_not hispanic or latino Stomach cancer

X365_splice 1 Splice white_not hispanic or latino Lung cancer

X366_splice 1 Splice asian_not hispanic or latino Stomach cancer

No data available Bladder cancer X420_splice 2 Splice asian_not hispanic or latino Uterine cancer

X514_splice 1 Splice white_not hispanic or latino Uterine cancer

Y123C 1 Missense No data available Breast cancer

Y404C 1 Missense not reported_not reported Lung cancer

Y337D 1 Missense white_not hispanic or latino Lung cancer

Y390* 1 Nonsense white_not hispanic or latino Thyroid cancer

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Table 2. Mutations found in three cancer genomics data repositories for Latin American populations.

Frequency Genomic DNA Database * Effect cancer type Population in the change AA change sample ICGC 22:29121093 S155F Missense Melanoma Brazil 1/70 (1.43%) 22:29138126 5 UTR 1/70 (1.43%) 22:29091835 I117M Missense 1/70 (1.43%) 22:29138096 5 UTR 1/70 (1.43%) TCGA 22:29130418 A98Mfs*13 Frameshift Diffuse large Latino 0,42% B-cell lymphoma [DLBC] 22:29130412 Q100* Nonsense Diffuse large Latino 0,42% B-cell lymphoma [DLBC] 22:29091840 K373E Missense Head and Latino 3,10% neck cancer ExAC 22:29090018 c.1590+2T>G splice donor NA Latino 8,81E-02 22:29090030 p.Pro527Leu missense NA Latino 0.0004404 22:29090054 p.Thr519Met missense NA Latino 8,81E-02 22:29091225 p.Ser465Asn missense NA Latino 8,99E-02 22:29091768 p.Val440PhefsTer17 frameshift NA Latino 8,66E-02 22:29091856 p.Thr410MetfsTer15* frameshift NA Latino 0.0001733 22:29092960 p.Gly385Ser missense NA Latino 8,76E-02 22:29099495 p.Glu345Asp missense NA Latino 0.0008772 22:29107942 p.Lys292Asn missense NA Latino 8,64E-02 22:29107982 p.Leu279Pro missense NA Latino 0.003112 22:29107994 p.Glu56Ter stop gained NA Latino 8,65E-02 22:29115401 p.Met265AsnfsTer24 frameshift NA Latino 0.0004003 22:29115403 p.Ile264Met missense NA Latino 0.0001938 22:29120978 p.Leu236LysfsTer4 frameshift NA Latino 8,65E-02 22:29121000 p.Asn229Ser missense NA Latino 0.0001729 22:29121078 p.Ile203Arg missense NA Latino 8,64E-02 22:29121229 c.573+2T>G splice donor NA Latino 8.64e-05 22:29121242 p.Arg188Trp missense NA Latino 8,64E-02 22:29121326 p.Arg160Gly missense NA Latino 8,65E-02 22:29130427 p.Arg95Ter stop gained NA Latino 8,67E-02 22:29130430 p.Ala94Ser missense NA Latino 8,67E-02 22:29130431 p.Trp93Ter stop gained NA Latino 8,67E-02 22:29130576 p.Thr45Met missense NA Latino 8,65E-02

*The nomenclature used for the mutation annotation is as follow: ICGC (ENST00000328354), ExAC (NP_665861) and TCGA (NP_009125).

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ICGC individuals with Han Chinese ancestry. In addition, in a Han A total of 279 mutations including up- and down-stream muta- Chinese cohort of esophageal and gastric cancer the mutation tions were reported in 185 donors. From this number, seven muta- rs738722-T was also associated with those cancers (Dataset 4). tions are predicted to have high impact (Table 3). For the Latin American population in ICGC, the Brazilian melanoma study Dataset 4. Variants reported in CHEK2 that have been associated (SKCA-BR) reported four mutations inside the gene, one of them with cancer according to data in the GWAS catalog with high impact (Table 2 and Table 3). http://dx.doi.org/10.5256/f1000research.9932.d142132

ExAC browser All of these variants were found in the cBioPortal or ICGC data. A total of 742 mutations for the CHEK2 gene were reported in this database and 132 of them were present in the Latino population before filters Dataset( 3). After applying the filter of possibly dam- CHEK2 mutations in Latinos reported in the literature aging and deleterious alterations, 23 mutations in the Latino popula- In total, we found nine studies in which mutations in CHEK2 tion were left. In this group the mutation p.Leu279Pro was the most were evaluated in Latino populations. Two of these studies were frequent (0.003112). CHEK2 c.1100delC (p.Thr410MetfsTer15*), international and included Latin American cancer patients10,22 and the most interrogated mutation in CHEK2, was found in two the other six studies were country-based. The country in which samples (Table 2). most studies have been performed was Brazil with four studies40–43. In Argentina44, Chile45, and Mexico46 one study per country was identified. In eight out of the nine studies, the presence of variants Dataset 3. Mutations in CHEK2 identified in Latino American in CHEK2 was interrogated in breast cancer patients. Only one samples before applying filters (source:ExAC) study used samples of patients with hereditary breast and colorectal http://dx.doi.org/10.5256/f1000research.9932.d142131 cancer. The mutation most frequently evaluated in these investiga- tions was c.1100delC (in six studies); while other two studies42,44 interrogated the other two most frequent mutations in the CHEK2 GWAS catalog gene (c.470T>C and c.444+IG>A) in addition to c.1100delC. Addi- Mutations rs132390-C and rs17879961-A mapped to or near tionally, Chaudury et al. performed a complete sequencing of the CHEK2 were associated in European populations with breast and gene and found a different mutation, c.478A>G (p.Arg160Gly)46. lung cancer, respectively. Mutations rs4822983-T and rs2239815-T Table 4 shows the Latin American studies that reported the were associated with esophageal squamous cell carcinoma in presence of mutations in CHEK2 mutations and their frequency.

Table 3. Mutations in CHEK2 (5’ to 3’UTR) with high impact in the ICGC portal excluding TCGA data.

Functional Genomic DNA change Consequences* Donors affected impact CHEK2 D229N, D47N, D205N, D39N, D75N, D296N, 22:29099515C>T high MELA-AU:1/183 D339N CHEK2 D202E, D36E, D336E, D293E, D226E, D72E, 22:29099522A>C high MELA-AU:1/183 D44E|3 UTR: CHEK2 |Exon: CHEK2 |Intron: CHEK2 CHEK2 L268F, L177F, L311F, L47F, L11F, L19F, 22:29106038G>A high MELA-AU:1/183 L201F |3 UTR: CHEK2 |Exon: CHEK2 |Intron: CHEK2 22:29121093G>A high Missense: CHEK2 S155F, S186F, S198F, S165F SKCA-BR:1/70 Missense: CHEK2 R188W, R176W, R145W, BRCA-EU:1/560|ESAD- 22:29121242G>A high R155W|Start Gained: CHEK2 |3 UTR: CHEK2 |Exon: UK:1/203 CHEK2 |Intron: CHEK2 CHEK2 S59F, S49F|5 UTR: CHEK2 |Exon: CHEK2 22:29130564G>A high LINC-JP:1/244 |Intron: CHEK2 Missense: CHEK2 R13W, R3W|Start Gained: CHEK2 BRCA-EU:1/560|BRCA- 22:29130703G>A high |Exon: CHEK2 |Intron: CHEK2 FR:1/64

*Depending of transcript. All mutations are single base substitutions. MELA-AU: melanoma, Australia. BRCA-EU: breast ER+ and HER- cancer, European Union. ESAD-UK: esophageal adenocarcinoma, United Kingdom. SKCA-BR: skin adenocarcinoma, Brazil. LINC-JP: liver cancer, Japan. BRCA-FR: breast cancer, France.

Page 9 of 19 Table 4. Mutations in CHEK2 reported in the literature for Latin American populations.

Position dbSNP Nucleotide AA change Effect Population Cancer type Carriers n (frequency of Source Reference change carriers, %) Case Control Latin- Breast cancer 1/362 0/384(0%) Blood Bell et al. (2007) American (0.0027) Brazil Breast cancer 1/155 0/377 (0%) Blood Zhang et al. (2008) (0.7%) Brazil Hereditary 1/59(1.7%) 0 Stop Breast and 22:29091857 rs555607708 c.1100delC p.Thr367Metfs Blood Abud et al. (2012) codon Colorectal cancer Brazil Breast cancer 1/7families 0 predisposition (14.3%) Blood Palmero et al. (2016) syndromes (BCPS) F1000Research 2016,5:2791Lastupdated:20JUN2019 22:29121326 rs28909982 c.478A>G p.Arg160Gly Missense Mexico Breast cancer 2/92(2.17%) 0 Blood Chaudury et al. (2013) Page 10of19 F1000Research 2016, 5:2791 Last updated: 20 JUN 2019

in these populations and its frequency decreases as you get to Dataset 5. Number of individuals per cancer study and ethnicity in the TCGA cohort the southern regions of Europe (Basque Country, Spain, and Italy)47. Taking into account the European genetic component of http://dx.doi.org/10.5256/f1000research.9932.d142133 Latin American populations, it is expected that if the frequency of c.1100delC is low in the Spanish population, in our mixed Only studies in which at least one mutation in CHEK2 was found were included. populations the frequency would be even lower.

Because cancer types other than breast and colorectal cancer, Discussion such as uterine, lung, bladder and head and neck cancer, pre- A search in cancer genomics data repositories and the literature sented mutations in CHEK2 in several populations, it is relevant to was performed to identify mutations in CHEK2 in different can- focus the search for mutations in these types of cancer in the Latin cer types, with specific emphasis on mutations found in Latino American populations. Additionally, the interrogation of CHEK2 American populations. The database with the most number of mutations in the Latin American population has been focused mutations reported in CHEK2 for Latino populations was ExAC mainly on the c.1100delC mutation, but the data obtained from with 132 mutations, followed by ICGC with four mutations, and the ExAC database showed that in Latin American samples there TCGA with three mutations. After filtering 30 mutations with high are 23 germline mutations (Table 2) that could generate cancer and medium impact according to the databases functional impact susceptibility. It would therefore be important to examine the categories were kept: seventeen missense, eight ‘stop gain’ muta- frequencies of these mutations in the Latin American population tions, one frameshift mutation, two mutations in the 5’UTR, and and its association with the development of cancer. two mutations in splice donor sites of CHEK2. These mutations included the most analyzed mutation of CHEK2, c.1100delC This study has limitations; for example, information about race and (p.Thr367Metfs) (Table 2). ethnicity was not available for at least 28 studies in the cBioPortal, and consequently some Latinos may be hidden in those studies. Worldwide, according to our findings in the ICGC and TCGA Thus, the small number of Latinos included in the genomics data databases, CHEK2 mutations were reported in 23 cancer types, repositories could be a reason why we have found a small number while in the Latin American population CHEK2 mutations were of mutations in CHEK2 in this population. It is important to only found in head and neck cancer, lymphoma and melanoma. highlight that the use of different transcripts for reporting muta- In this context, it is important to highlight, that Latino populations tions makes the correlation between mutations found in different have been underrepresented in other worldwide studies. As shown in studies laborious. Dataset 4, the cohorts of TCGA are biased toward the inclu- sion of white individuals and individuals from other ethnicities This study presents a compilation of mutations in CHEK2 with are underrepresented. The same was observed in ICGC in which high impact in different cancer types in White, Hispanic and other only a Latin American cohort from Brazil was available for our populations. We also showed the necessity of performing studies in analysis. Regarding the data found in our literature review, CHEK2 Latin American in cancer types different than breast and colorectal has only been studied in the Latin American population in breast and a screening of other mutations in addition to the most popular and colorectal cancer. mutations analyzed, such as c.1100delC.

In the ExAC repository, the mutations c.1100delC and c.478A>G Data availability were found two times and one time, respectively, in the Latino F1000Research: Dataset 1: A complete list of mutations, before population (Dataset 3). In TCGA, c.1100delC was found in a applying filters, in CHEK2 reported in the cBioPortal 10.5256/ patient with breast cancer but information about its ethnicity f1000research.9932.d14212948. was not available (Table 1). Up to now, only nine studies evalu- ating mutations in CHEK2 have been performed in Latin Amer- F1000Research: Dataset 2: A complete list of mutations, before ica and only six of them found mutations in the gene, five stud- applying filters, in CHEK2 reported in the ICGC 10.5256/ 49 ies found the c.1100delC mutation and one found the c.478A>G f1000research.9932.d142130 . (p.Arg160Gly)10,22,40,43,46. Two mutations, c.1100delC and F1000Research: Dataset 3: Mutations in CHEK2 identified in c.478A>G, were classified in the ClinVar archivehttps://www. ( Latino American samples before applying filters (source:ExAC) ncbi.nlm.nih.gov/clinvar/) as pathogenic and likely pathogenic, 10.5256/f1000research.9932.d14213150. respectively. These mutations are the only ones in common with the mutations found in genomics data repositories. F1000Research: Dataset 4: Variants reported in CHEK2 that have been associated with cancer according to data in the GWAS catalog. All of these variants were found in the cBioPortal or Although c.1100delC is the CHEK2 mutation most evaluated in ICGC data 10.5256/f1000research.9932.d14213251. the Latin American population, it should be noted that its frequency, seen from literature reports and data repositories, is F1000Research: Dataset 5: Number of individuals per cancer rather low. Because the highest frequency of this mutation is found study and ethnicity in the TCGA cohort. Only studies in which at in populations from the Northern and Western Europe, c.1100delC least one mutation in CHEK2 was found were included 10.5256/ is proposed as an allele with population gradient, which originated f1000research.9932.d14213352.

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Author contributions Competing interests Conception and design of the work: CCL, GOS and RHAL. Data No competing interests were disclosed. collection: RHAL and GOS. Data analysis: CCL, GOS and RHAL. Drafting of the article and critical revision: CCL, GOS, and RHAL. Grant information All authors were involved in the revision of the draft manuscript LCC received funding by CONADI-Universidad Cooperativa de and have agreed to the final content. Colombia (Grant ID1450).

References

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Kleibl Z, Novotny J, Bezdickova D, et al.: The CHEK2 c.1100delC germline Portal--a one-stop shop for cancer genomics data. Database (Oxford). 2011; mutation rarely contributes to breast cancer development in the Czech 2011: bar026. Republic. Breast Cancer Res Treat. 2005; 90(2): 165–7. PubMed Abstract | Publisher Full Text | Free Full Text PubMed Abstract | Publisher Full Text 33. Welter D, MacArthur J, Morales J, et al.: The NHGRI GWAS Catalog, a curated 16. Offit K, Pierce H, Kirchhoff T,et al.: Frequency of CHEK2*1100delC in New York resource of SNP-trait associations. Nucleic Acids Res. 2014; 42(Database issue): breast cancer cases and controls. BMC Med Genet. 2003; 4: 1. D1001–6. PubMed Abstract | Publisher Full Text | Free Full Text PubMed Abstract | Publisher Full Text | Free Full Text 17. Rashid MU, Jakubowska A, Justenhoven C, et al.: German populations with 34. Shihab HA, Gough J, Cooper DN, et al.: Predicting the functional, molecular, and

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phenotypic consequences of amino acid substitutions using hidden Markov 44. Jablonski P, Alterman L, Pastene E, et al.: Argentinean Jewish population models. Hum Mutat. 2013; 34(1): 57–65. frequencies for common mutations in BRCA1, BRCA2, and CHEK2. Journal of PubMed Abstract | Publisher Full Text | Free Full Text Clinical Oncology. [Abstract]. 2014; 32(15_suppl): 1539. 35. Reva B, Antipin Y, Sander C: Predicting the functional impact of protein Reference Source mutations: application to cancer genomics. Nucleic Acids Res. 2011; 39(17): e118. 45. Gonzalez-Hormazabal P, Castro VG, Blanco R, et al.: Absence of CHEK2 PubMed Abstract | Publisher Full Text | Free Full Text 1100delC mutation in familial breast cancer cases from a South American 36. Ng PC, Henikoff S: Predicting deleterious amino acid substitutions. Genome population. Breast Cancer Res Treat. 2008; 110(3): 543–5. Res. 2001; 11(5): 863–74. PubMed Abstract | Publisher Full Text PubMed Abstract | Publisher Full Text | Free Full Text 46. Chaudhury A, Laukaitis C, Mauss C, et al.: Abstract P3-07-05: Frequent BRCA1 37. Adzhubei IA, Schmidt S, Peshkin L, et al.: A method and server for predicting and BRCA2 mutations are found in Mexican and Mexican-American women damaging missense mutations. Nat Methods. 2010; 7(4): 248–9. with breast cancer. Cancer Research. 2013; 73(24_suppl). PubMed Abstract | Publisher Full Text | Free Full Text Publisher Full Text 38. McLaren W, Gil L, Hunt SE, et al.: The Ensembl Variant Effect Predictor. Genome 47. Martínez-Bouzas C, Beristain E, Guerra I, et al.: CHEK2 1100delC is present in Biol. 2016; 17(1): 122. familial breast cancer cases of the Basque Country. Breast Cancer Res Treat. PubMed Abstract | Publisher Full Text | Free Full Text 2007; 103(1): 111–3. PubMed Abstract Publisher Full Text 39. R Core Team: R: A language and environment for statistical computing. | R Foundation for Statistical Computing, Vienna, Austria, 2013. 48. Guauque-Olarte S, Rivera-Herrera AL, Cifuentes-C L: Dataset 1 in: Mutations Reference Source of the CHEK2 gene in patients with cancer and their presence in the Latin American population. F1000Research. 2016. 40. Abud J, Koehler-Santos P, Ashton-Prolla P, et al.: CHEK2 1100DELC germline Data Source mutation: a frequency study in hereditary breast and colon cancer Brazilian families. Arq Gastroenterol. 2012; 49(4): 273–8. 49. Guauque-Olarte S, Rivera-Herrera AL, Cifuentes-C L: Dataset 2 in: Mutations PubMed Abstract | Publisher Full Text of the CHEK2 gene in patients with cancer and their presence in the Latin American population. F1000Research. 2016. 41. Carraro DM, Koike Folgueira MA, Garcia Lisboa BC, et al.: Comprehensive Data Source analysis of BRCA1, BRCA2 and TP53 germline mutation and tumor characterization: a portrait of early-onset breast cancer in Brazil. PLoS One. 50. Guauque-Olarte S, Rivera-Herrera AL, Cifuentes-C L: Dataset 3 in: Mutations 2013; 8(3): e57581. of the CHEK2 gene in patients with cancer and their presence in the Latin PubMed Abstract | Publisher Full Text | Free Full Text American population. F1000Research. 2016. Data Source 42. Felix GE, Abe-Sandes C, Machado-Lopes TM, et al.: Germline mutations in BRCA1, BRCA2, CHEK2 and TP53 in patients at high-risk for HBOC: 51. Guauque-Olarte S, Rivera-Herrera AL, Cifuentes-C L: Dataset 4 in: Mutations characterizing a Northeast Brazilian Population. Hum Genome Var. 2014; 1: 14012. of the CHEK2 gene in patients with cancer and their presence in the Latin PubMed Abstract | Publisher Full Text | Free Full Text American population. F1000Research. 2016. Data Source 43. Palmero EI, Alemar B, Schuler-Faccini L, et al.: Screening for germline BRCA1, BRCA2, TP53 and CHEK2 mutations in families at-risk for hereditary breast 52. Guauque-Olarte S, Rivera-Herrera AL, Cifuentes-C L: Dataset 5 in: Mutations cancer identified in a population-based study from Southern Brazil. Genet Mol of the CHEK2 gene in patients with cancer and their presence in the Latin Biol. 2016; 39(2): 210–22. American population. F1000Research. 2016. PubMed Abstract | Publisher Full Text | Free Full Text Data Source

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Open Peer Review

Current Peer Review Status:

Version 1

Reviewer Report 24 April 2017 https://doi.org/10.5256/f1000research.10703.r22138

© 2017 Grzybowska E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Ewa Grzybowska Center for Translational Research and Molecular Biology of Cancer, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Wybrzeze Armii Krajowej 15, 44-101, Gliwice, Poland

The authors worked out the compilation of germline mutations in the CHEK2 gene in patients diagnosed with different cancer types and in different populations focusing on Latin American population. CHEK2 mutations have been linked with Li—Fraumeni syndrome, also germline mutations are thought to confer a predisposition to sarcomas, breast cancer and brain tumors. The most frequent CHEK2 mutation c.1100 delC is the low penetrance mutation and it has low impact in breast or other cancers risk. The rest of the mutations or SNPs are much less connected with the known cancer risk. Therefore, it is difficult to evaluate the increase of the different cancers risk for the carriers of germline mutations in CHEK2 gene. It is particularly difficult to do so if there are only one or two carriers of these mutations in the population under study.

I totally agree with both reviewers especially with two issues: 1. The text is written in that way that the reader can think the authors analyze somatic mutations in CHEK2 gene in different cancer types whereas they made the search for germline mutations. In Table 4 they indicate that the blood was the tissue which was used to analyze mutations so the text should be rewritten in that way there would be no doubt that germline mutations were under study. 2. The Tables 1 and 2 also should be changed. The description of the ethnic minorities is strange. In Table 2 the data from databaseExAC do not contain the information about the disease connected with the mutation so it does not make sense to include these data if the title of the manuscript is “Mutations of the CHEK2 gene in the patients with cancer…” these data should be excluded from the analysis because they do not bring any important information about CHEK2 mutations in different cancer sites.

Is the work clearly and accurately presented and does it cite the current literature? No

Is the study design appropriate and is the work technically sound?

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Are sufficient details of methods and analysis provided to allow replication by others? Partly

If applicable, is the statistical analysis and its interpretation appropriate? Yes

Are all the source data underlying the results available to ensure full reproducibility? Yes

Are the conclusions drawn adequately supported by the results? Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: cancer genetics, molecular biology of carcinogenesis, epidemiology of cancer, pharmacogenetics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Reviewer Report 03 April 2017 https://doi.org/10.5256/f1000research.10703.r21485

© 2017 Rashid M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Muhammad Usman Rashid Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH & RC), Department of Basic Sciences Research, Lahore, Pakistan

The manuscript by Guauque-Olarte and colleagues is an overview of the CHEK2 variants reported in Latin American population, searched from literature or cBioPortal, ICGC and ExAC databases. Overall the concept of the manuscript is interesting; however the data is poorly presented and the scientific writing is not up to the mark. The manuscript title also needs modification, like “An overview of CHEK2 variants associated with cancer in Latin American population”.

I have following reservations about the manuscript: 1. For missense variants or variants in 5’UTRs it is suggested to write “DNA sequence variants” instead of “mutations” throughout the manuscript, so that these can be differentiated from clear pathogenic mutations i.e. frameshift, nonsense or splice site mutations. 2. As the study objective was to compile the CHEK2 mutations reported in Latin Americans, Table 1 describes the CHEK2 variants identified in other populations or even the ethnicity is unknown for majority of the variants presented in this table. The table is also not presented properly. It is suggested to omit this table or present it as a “Dataset”, and just mention in the text that 78

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suggested to omit this table or present it as a “Dataset”, and just mention in the text that 78 deleterious or potentially deleterious mutations were reported in TCGA studies. 3. The authors did not state about the origin (somatic or germline) of CHEK2 variants presented in all the tables. It would be of interest if a column is added in all tables for this information. 4. Results section: Data presented in Figure 1 and Figure 2 is not concordant as mentioned in the text. Please resolve this issue. 5. Results section: “…..after the filtering process, 38 of which were classified as with high impact” It is not clear which those 38 nucleotide variants are in Table 1? Please add a column for this information. 6. Results section: Paragraph “Two patients with three mutations …….this patient carry a frameshift and a nonsense mutation” is confusing. Is the patient with DLBC a compound heterozygous for a frameshift and nonsense CHEK2 mutation, simultaneously? 7. Table 2: Column Genomic DNA change: The nucleotide change can’t be seen in this column, there is just the nucleotide position. Please modify this column. 8. Results section: GWAS catalog. Authors should be cautious whether the SNPs rs132390-C and rs2239815-T are present in CHEK2 gene or not? 9. Table 2: Two variants in 5’UTR are not clear, population is also not mentioned. 10. Table 2: “Effect” column; please correct that stop gain mutations are also called nonsense mutations. 11. Table 2: The data in the table is not presented properly. c.1590+2T>G and c.573+2T>G are the nucleotide changes and these are presented in column AA change. The authors should follow HGVS nomenclature, both for nucleotide change and the AA change. There should be a column for pathogenicity of missense mutations (high or medium impact) in this table. 12. Table 3: Column Consequences: I think there is no need to mention the amino acid change referring all CHEK2 transcripts. Just follow the GenBank reference sequence for transcript variant 1 for reporting nucleotide or AA change and follow the HGVS nomenclature. 13. Discussion, paragraph 1: “….eight stop gain mutations, one frameshift mutation…” Please correct, there are four stop gain mutations (also called nonsense mutations) and five frameshift mutations.

Is the work clearly and accurately presented and does it cite the current literature? Partly

Is the study design appropriate and is the work technically sound? Partly

Are sufficient details of methods and analysis provided to allow replication by others? Partly

If applicable, is the statistical analysis and its interpretation appropriate? Partly

Are all the source data underlying the results available to ensure full reproducibility? Partly

Are the conclusions drawn adequately supported by the results? Partly

Competing Interests: No competing interests were disclosed.

I confirm that I have read this submission and believe that I have an appropriate level of

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Reviewer Report 03 March 2017 https://doi.org/10.5256/f1000research.10703.r20668

© 2017 Palles C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Claire Palles Wellcome Trust Centre for Human Genetics, NIHR Comprehensive Biomedical Research Centre, Oxford, UK

Sandra Guauque-Olarte et al provide an overview of both somatic and germline mutations in CHEK2 that have been identified in Latin-American populations. The authors interrogate cBioPortal and ICGC databases to identify somatic mutations and ExAC and a review of existing literature to identify germline mutations.

My reservations about the manuscript are as follows:

Currently the authors do not make it clear throughout the manuscript whether they are describing somatic mutations or germline mutations/variants. Please add a column to table 2 to show clearly which are somatic and which are germline.

In the abstract it says: “Latin American studies have been restricted to breast and colorectal cancer and only two mutations out of four that have been interrogated in this population were identified, namely c.1100delC and c.349A>G”. Table 4 which lists the mutations reported in the literature in Latin American studies does not show the c.349A>G mutation but a c.478A>G mutation and I can see no further mention of c.349A>G in the rest of the manuscript. Please resolve this.

Results: The text description of the difference between Figure 1 and 2 in the start of the results section is unclear. As far as I can see Figure 1 shows data per cancer type in TCGA and Figure 2 shows data per study in TCGA. I don’t see the need to have both figures- figure 1 is sufficient and the text should read “breast, colorectal and non small cell lung cancer had more CHEK2 mutations than other cancer type”. At the start of the results section the authors describe mutations “before filtering”. Please be clearer and state before filtering steps to include only likely functional mutations.

On page 5 the sentence beginning The type of cancer with the most mutations …. should read “After filtering for likely functional variants the cancers with the highest numbers of mutations in CHEK2 were breast followed by uterine, non small cell (?) lung and colorectal.

Table 1 describes mutations in non Hispanic-latino samples. A98Mfs*13 and Q100*, which are found in a white Hispanic or latino sample should be removed (none of the other mutations in Latin American populations are in Table 1). The ethnicity column in table 1 also needs to be formatted properly – remove duplicated words and “_” between words.

Page 17 of 19 F1000Research 2016, 5:2791 Last updated: 20 JUN 2019 duplicated words and “_” between words.

GWAS catalogue section in results- need to insert P-values for the associations that you report. Table 4. Insert OR and P-values for associations.

Dataset 5. The %s should not have a – infront of them, it adds confusion as to what these values are.

Discussion:

The authors make reference to a CHEK2 1100delC mutation picked up in the TCGA datasets and refer to Table 1. I cant find 1100delC in table 1. I can only find it in Table 2 in ExAC. Please clarify.

The authors state that other patients with cancer types such as uterine, lung, bladder and head and neck cancer should be screened for CHEK2 mutations. Here they are trying to show that because a gene is somatically mutated in a particular cancer type that there might also be a germline mutation that increases predisposition. Some of the mutations listed in tables 1 and 2 (mutations post filtering for likely functional impact) are missense or UTR and so it would be important to show that these somatic mutations are functional. Could the authors please annotate the TCGA/ICGC mutations with information of which domain they map to.

The authors state there are 23 germline mutations which could cause cancer susceptibility. Were any of these examined for a functional affect on CHECK2 in paper by Bell et al 20071 or other studies of variants in CHEK2 on protein activity? I think it would be important to include this and to also state that functional assays would be helpful to determine which of these should be screened for in Latin American and other populations.

References 1. Bell DW, Kim SH, Godwin AK, Schiripo TA, Harris PL, Haserlat SM, Wahrer DC, Haiman CA, Daly MB, Niendorf KB, Smith MR, Sgroi DC, Garber JE, Olopade OI, Le Marchand L, Henderson BE, Altshuler D, Haber DA, Freedman ML: Genetic and functional analysis of CHEK2 (CHK2) variants in multiethnic cohorts.Int J Cancer. 2007; 121 (12): 2661-7 PubMed Abstract | Publisher Full Text

Is the work clearly and accurately presented and does it cite the current literature? Partly

Is the study design appropriate and is the work technically sound? Partly

Are sufficient details of methods and analysis provided to allow replication by others? Partly

If applicable, is the statistical analysis and its interpretation appropriate? Partly

Are all the source data underlying the results available to ensure full reproducibility? Partly

Are the conclusions drawn adequately supported by the results?

Page 18 of 19 F1000Research 2016, 5:2791 Last updated: 20 JUN 2019

Are the conclusions drawn adequately supported by the results? Partly

Competing Interests: No competing interests were disclosed.

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